The field of dermatological image classification has greatly advanced by adopting Machine Learning and Artificial Intelligence. However, it faces some challenges and gaps, such as a lack of diversity in classification models, which leads to misclassification and a lack of efficiency. This research aims at enhancing the correct classification of dermatological images by considering skin tone diversity. A combination of the Fitzpatrick 17k and DDI datasets is used and because of their diversity, transfer learning using EfficientNetB0 is the preferred classification model. Two machine-learning models are created, trained, and tested using images from different skin tones, and the results are evaluated.